非计划再入院患者的分布特征及影响因素

Distribution Characteristics and Influencing Factors of Unplanned Readmission Patients

目的 探讨某三级综合医院2020年1月1日-2020年12月31日期间住院患者非计划再入院的影响因素,为减少重复入院提供依据,提高医疗资源的利用效率和医院管理质量。方法 采用Excel 2013软件对某院2020年1月1日-2020年12月31日期间住院患者数据75810人次进行初步整理和分析,利用SPSS25.0软件先对各影响因素进行统计描述、单因素关联分析,将单因素分析结果中P<0.05的变量纳入多因素二分类Logistic回归。结果 非计划再入院患者2016人次,非计划再入院率2.66%。相对于男性患者,女性降低了非计划再入院的风险(OR=0.82,95%CI 0.75-0.90, P<0.05),住院天数是非计划再入院的保护因素(OR=0.97,95%CI 0.96-0.98, P<0.05),其他离院方式(OR=1.05,95%CI 1.01-1.10,P=0.03)、手术(OR=1.93,95%CI 1.63-2.27, P<0.05)增加了非计划再入院的风险;年龄组、疾病分型和手术级别与非计划再入院呈正相关,差异具有统计学意义。结论 非计划再入院的主要影响因素包括患者性别、年龄组、离院方式、疾病分型、是否手术、手术级别、住院天数。医院应重视非计划再入院指标,加强非计划再入院的管理和诊疗规范,增强老年患者的健康宣教,制定合理的监管指标和考核体系,有效降低非计划再入院率。

Objectives This study aims to explore the influencing factors of unplanned readmission of inpatients in a tertiary general hospital from January 1, 2020 to December 31, 2020, so as to provide a basis for reducing repeated admissions and improving the utilization efficiency of medical resources and the quality of hospital management. Methods This study used Excel 2013 software to conduct a preliminary collation and analysis of the data of 75 810 inpatients in a hospital during the period from January 1, 2020 to December 31, 2020, used SPSS25.0 software to perform statistical description and univariate correlation analysis on each influencing factor and included variables with P<0.05 in the univariate analysis results into a multi-factor binary logistic regression.Results A total of 75,810 patients were discharged from a tertiary general hospital from January 1 to December 31,2020, and 2,016 patients had unplanned readmissions, with an unplanned readmission rate of 2.66%. Compared with male patients, female patients had a reduced risk of unplanned readmission(OR=0.82, 95%CI 0.75-0.90,P<0.05). The length of hospital stay was a protective factor for unplanned readmission(OR=0.97, 95%CI 0.96-0.98, P<0.05). Other discharge methods(OR=1.05, 95%CI 1.01-1.10, P=0.03) and surgery(OR =1.93, 95%CI 1.63-2.27, P<0.05) increased the risk of unplanned readmission. Age group, disease type, and surgical grade were positively correlated with unplanned readmission, and the difference was statistically significant. Conclusions The main influencing factors of unplanned readmission included patient’s gender, age group, discharge method, disease type, operation or not, operation level, and length of hospital stay. Hospitals should attach importance to the index of unplanned readmission, strengthen the management and the treatment norm of unplanned readmissions, enhance the health education of elderly patients, formulate reasonable supervision indicators and assessment system to effectively reduce the rate of unplanned readmission.